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The impact of indirect machine translation on sentiment classification

Poncelas, Alberto orcid logoORCID: 0000-0002-5089-1687, Lohar, Pintu orcid logoORCID: 0000-0002-5328-1585, Way, Andy orcid logoORCID: 0000-0001-5736-5930 and Hadley, James orcid logoORCID: 0000-0003-1950-2679 (2020) The impact of indirect machine translation on sentiment classification. In: 14th biennial conference of the Association for Machine Translation in the Americas, AMTA, 6-10 Oct 2020, Orlando, Fl, USA (Virtual).

Sentiment classification has been crucial for many natural language processing (NLP) applications, such as the analysis of movie reviews, tweets, or customer feedback. A sufficiently large amount of data is required to build a robust sentiment classification system. However, such resources are not always available for all domains or for all languages. In this work, we propose employing a machine translation (MT) system to translate customer feedback into another language to investigate in which cases translated sentences can have a positive or negative impact on an automatic sentiment classifier. Furthermore, as performing a direct translation is not always possible, we explore the performance of automatic classifiers on sentences that have been translated using a pivot MT system. We conduct several experiments using the above approaches to analyse the performance of our proposed sentiment classification system and discuss the advantages and drawbacks of classifying translated sentences.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Subjects:Computer Science > Computational linguistics
Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Published in: Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (AMTA 2020). . Association for Machine Translation in the Americas (AMTA).
Publisher:Association for Machine Translation in the Americas (AMTA)
Official URL:https://www.aclweb.org/anthology/2020.amta-researc...
Copyright Information:© 2020 The Authors. CC-BY- 4.0
Funders:SFI Research Centres Programme (Grant 13/RC/2106), Irish Research Council’s COALESCE scheme (COALESCE/2019/117)
ID Code:24951
Deposited On:27 Aug 2020 13:32 by Alberto Poncelas . Last Modified 05 May 2023 16:32

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